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dsh-geometric-mean

Model Inference Updated 2026.08.21

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-geometric-mean

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install uckkk/dsh-geometric-mean in the DeepSeek Harness terminal to install this plugin; the full source is available at https://github.com/uckkk/dsh-geometric-mean

About this plugin

When you benchmark model inference, you often need to fold several metrics—perplexity, time-to-first-token, throughput—into a single composite score. Arithmetic means are easily distorted by a single outlier, while the geometric mean naturally penalises any weak dimension, making it the more reliable aggregator for inference-quality metrics.

dsh-geometric-mean is a tiny, pure Node.js library that computes the geometric mean of a numeric array. It brings zero external dependencies and makes zero network calls, so you can drop it into an inference pipeline, an evaluation script, or a CI job without worrying about supply-chain bloat or connectivity. The function is deterministic: same input, same output, every time.

If you build LLM inference benchmarks, write automated scoring scripts, or simply need an offline, reproducible way to aggregate multi-dimensional metrics without pulling in a heavyweight math library, this plugin gives you a one-import, one-call solution with no configuration overhead.

Use Cases

  • Aggregating latency, throughput, and accuracy into a single composite inference score
  • Calling a scoring function inside an offline CI pipeline with zero network requests
  • Replacing arithmetic mean in evaluation scripts to resist outlier distortion

Best For

  • Engineers building LLM inference benchmarks
  • Data engineers writing automated model-scoring scripts
  • Researchers aggregating multi-dimensional metrics in offline environments